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Image anti-forensics is a critical topic in the field of image privacy and security research. With the increasing ease of manipulating or generating human faces in images, the potential misuse of such forged images is a growing concern. This study aims to comprehensively review the knowledge structure and research hotspots related to image anti-forensics by analyzing publications in the Web of Science Core Collection (WoSCC) database. The bibliometric analysis conducted using VOSViewer software has revealed the research trends, major research institutions, most influential publications, top publishing venues, and most active contributors in this field. This is the first comprehensive bibliometric study summarizing research trends and developments in image anti-forensics. The information highlights recent and primary research directions, serving as a reference for future research in image anti-forensics.

Accurately recommending personalized candidate news articles to users has always been the core challenge of news recommendation system. News recommendations often require modeling of user interests to match candidate news. Recent efforts have primarily focused on extract local subgraph information, the lack of a comprehensive global news graph extraction has hindered the ability to utilize global news information collaboratively among similar users. To overcome these limitations, we propose an effective and efficient Long Interest Chain Modeling for News Recommendation(LICM), which combines neighbor interest with long-chain interest distilled from a global news click graph based on the collaborative of similar users to enhance news recommendation. For a global news graph based on the click history of all users, long chain interest generated from it can better utilize the high-dimensional information within it, enhancing the effectiveness of collaborative recommendations. We therefore design a comprehensive selection mechanism and interest encoder to obtain long-chain interest from the global graph. Finally, we use a gated network to integrate long-chain information with neighbor information to achieve the final user representation. Experiment results on real-world datasets validate the effectiveness and efficiency of our model to improve the performance of news recommendation.

To detect stego (steganographic text) in complex scenarios, linguistic steganalysis (LS) with various motivations has been proposed and achieved excellent performance. However, with the development of generative steganography, some stegos have strong concealment, especially after the emergence of LLMs-based steganography, the existing LS has low detection or cannot detect them. We designed a novel LS with two modes called LSGC. In the generation mode, we created an LS-task "description" and used the generation ability of LLM to explain whether texts to be detected are stegos. On this basis, we rethought the principle of LS and LLMs, and proposed the classification mode. In this mode, LSGC deleted the LS-task "description" and used the "causalLM" LLMs to extract steganographic features. The LS features can be extracted by only one pass of the model, and a linear layer with initialization weights is added to obtain the classification probability. Experiments on strongly concealed stegos show that LSGC significantly improves detection and reaches SOTA performance. Additionally, LSGC in classification mode greatly reduces training time while maintaining high performance.

Currently, most methods for text steganalysis are based on deep neural networks (DNNs). However, in real-life scenarios, obtaining a sufficient amount of labeled stego-text for correctly training networks using a large number of parameters is often challenging and costly. Additionally, due to a phenomenon known as dataset bias or domain shift, recognition models trained on a large dataset exhibit poor generalization performance on novel datasets and tasks. Therefore, to address the issues of missing labeled data and inadequate model generalization in text steganalysis, this paper proposes a cross-domain stego-text analysis method (PDTS) based on pseudo-labeling and domain adaptation (unsupervised learning). Specifically, we propose a model architecture combining pre-trained BERT with a single-layer Bi-LSTM to learn and extract generic features across tasks and generate task-specific representations. Considering the differential contributions of different features to steganalysis, we further design a feature filtering mechanism to achieve selective feature propagation, thereby enhancing classification performance. We train the model using labeled source domain data and adapt it to target domain data distribution using pseudo-labels for unlabeled target domain data through self-training. In the label estimation step, instead of using a static sampling strategy, we propose a progressive sampling strategy to gradually increase the number of selected pseudo-label candidates. Experimental results demonstrate that our method performs well in zero-shot text steganalysis tasks, achieving high detection accuracy even in the absence of labeled data in the target domain, and outperforms current zero-shot text steganalysis methods., Comment: The 30th International Conference on Computational & Experimental Engineering and Sciences (ICCES2024)

While Large Language Models (LLMs) demonstrate exceptional performance in a multitude of Natural Language Processing (NLP) tasks, they encounter challenges in practical applications, including issues with hallucinations, inadequate knowledge updating, and limited transparency in the reasoning process. To overcome these limitations, this study innovatively proposes a collaborative training-free reasoning scheme involving tight cooperation between Knowledge Graph (KG) and LLMs. This scheme first involves using LLMs to iteratively explore KG, selectively retrieving a task-relevant knowledge subgraph to support reasoning. The LLMs are then guided to further combine inherent implicit knowledge to reason on the subgraph while explicitly elucidating the reasoning process. Through such a cooperative approach, our scheme achieves more reliable knowledge-based reasoning and facilitates the tracing of the reasoning results. Experimental results show that our scheme significantly progressed across multiple datasets, notably achieving over a 10% improvement on the QALD10 dataset compared to the best baseline and the fine-tuned state-of-the-art (SOTA) work. Building on this success, this study hopes to offer a valuable reference for future research in the fusion of KG and LLMs, thereby enhancing LLMs' proficiency in solving complex issues.

Linguistic steganography (LS) tasks aim to generate steganographic text (stego) based on secret. Only authorized receivers can perceive and extract secrets, thereby protecting privacy. However, existing generative LS schemes often do not consider the conditional probability of tokens in the candidate pool, and allocate one or the same number of codings to all tokens. The tokens with lower probabilities are selected to embed secrets that will affect the quality of stegos. As a result, the stegos are easy to perceive and detect. This paper proposes the LS scheme based on dynamically allocated intervals, called DAIRstega. DAIRstega uses the idea of the roulette wheel and takes the conditional probabilities of tokens as the main basis for allocating the roulette area (i.e., the interval length). Thus, the token with a larger conditional probability is allocated more. The secret will be more likely to select the tokens with larger probabilities. In the allocation process, we design some functions between probability and allocated interval length. Based on the invisible characteristics of LS, we give three constraints that need to be met to design the function. To simplify the form, the expression of the allocation function is condensed. Furthermore, DAIRstega can receive additional instruction and controllably generate stegos. Rich experiments show that the proposed embedding way and DAIRstega perform superior to the existing ways and LS schemes, which shows strong perceptual, statistical, and semantic concealment and anti-steganalysis ability. This scheme can also generate high-quality longer stegos, improving the deficiencies in this task. The experiment also verified that DAIRstega can be used as a secure watermarking scheme, providing some ideas for its development., Comment: 12 pages

Linguistic steganalysis (LS) tasks aim to detect whether a text contains secret information. Existing LS methods focus on the deep-learning model design and they achieve excellent results in ideal data. However, they overlook the unique user characteristics, leading to weak performance in social networks. And a few stegos here that further complicate detection. We propose the UP4LS, a framework with the User Profile for enhancing LS in realistic scenarios. Three kinds of user attributes like writing habits are explored to build the profile. For each attribute, the specific feature extraction module is designed. The extracted features are mapped to high-dimensional user features via the deep-learning model of the method to be improved. The content feature is extracted by the language model. Then user and content features are integrated. Existing methods can improve LS results by adding the UP4LS framework without changing their deep-learning models. Experiments show that UP4LS can significantly enhance the performance of LS-task baselines in realistic scenarios, with the overall Acc increased by 25%, F1 increased by 51%, and SOTA results. The improvement is especially pronounced in fewer stegos. Additionally, UP4LS also sets the stage for the related-task SOTA methods to efficient LS., Comment: 15 pages, 7 figures, 14 tables

MeV ultrafast electron diffraction (UED) is a widely used technology for ultrafast structural dynamic studies of matters in numerous areas. The development of laser wakefield accelerator (LWFA) envisions great potential of advanced all-optical electron source based on LWFA in UED applications. We experimentally demonstrated that an LWFA-based device with a miniaturized permanent magnet beamline can generate and manipulate electron beams suitable for UED. In the beam transmission, the LWFA electron beams with intrinsic short duration stretch due to energy spread and then are compressed by a following double bend achromat. The optimized double bend achromat can make the beamline isochronous such that the arrival time jitter induced by the shot-to-shot energy fluctuation can be eliminated, and allow the advantage of the natural laser-beam synchronization for LWFAs to emerge. With the energy filtering, the beam energy spread can be reduced to 3% (FWHM) while a sufficient amount of charge (11.9 fC) per bunch for diffraction is retained. Start-to-end simulations showed that the bunch length reaches ~30 fs (rms) with the same experimental configuration. Clear single-shot and multi-shot diffraction patterns of single-crystalline gold samples are obtained and the derived lattice constant agrees excellently with the real value. Our proof-of-principle experiments open the door to the detection of ultrafast structural dynamics using MeV LWFA beams, and pave the way for the UED applications with sub-10-fs temporal resolution., Comment: 10 pages, 5 figures

Cyber threat hunting is a proactive search process for hidden threats in the organization's information system. It is a crucial component of active defense against advanced persistent threats (APTs). However, most of the current threat hunting methods rely on Cyber Threat Intelligence(CTI), which can find known attacks but cannot find unknown attacks that have not been disclosed by CTI. In this paper, we propose LogKernel, a threat hunting method based on graph kernel clustering which can effectively separates attack behaviour from benign activities. LogKernel first abstracts system audit logs into Behaviour Provenance Graphs (BPGs), and then clusters graphs by embedding them into a continuous space using a graph kernel. In particular, we design a new graph kernel clustering method based on the characteristics of BPGs, which can capture structure information and rich label information of the BPGs. To reduce false positives, LogKernel further quantifies the threat of abnormal behaviour. We evaluate LogKernel on the malicious dataset which includes seven simulated attack scenarios and the DAPRA CADETS dataset which includes four attack scenarios. The result shows that LogKernel can hunt all attack scenarios among them, and compared to the state-of-the-art methods, it can find unknown attacks.

Advanced Persistent Threat (APT) attack, also known as directed threat attack, refers to the continuous and effective attack activities carried out by an organization on a specific object. They are covert, persistent and targeted, which are difficult to capture by traditional intrusion detection system(IDS). The traffic generated by the APT organization, which is the organization that launch the APT attack, has a high similarity, especially in the Command and Control(C2) stage. The addition of features for APT organizations can effectively improve the accuracy of traffic detection for APT attacks. This paper analyzes the DNS and TCP traffic of the APT attack, and constructs two new features, C2Load_fluct (response packet load fluctuation) and Bad_rate (bad packet rate). The analysis showed APT attacks have obvious statistical laws in these two features. This article combines two new features with common features to classify APT attack traffic. Aiming at the problem of data loss and boundary samples, we improve the Adaptive Synthetic(ADASYN) Sampling Approach and propose the PADASYN algorithm to achieve data balance. A traffic classification scheme is designed based on the AdaBoost algorithm. Experiments show that the classification accuracy of APT attack traffic is improved after adding new features to the two datasets so that 10 DNS features, 11 TCP and HTTP/HTTPS features are used to construct a Features set. On the two datasets, F1-score can reach above 0.98 and 0.94 respectively, which proves that the two new features in this paper are effective for APT traffic detection.

Traditional steganalysis methods generally include two steps: feature extraction and classification.A variety of steganalysis algorithms based on CNN (Convolutional Neural Network) have appeared in recent years. Among them, the convolutional layer of the CNN model is usually used to extract steganographic features, and the fully connected layer is used for classification. Because the effectiveness of feature extraction seriously influences the accuracy of classification, designers generally improve the accuracy of steganographic detection by improving the convolutional layer. For example, common optimizing methods in convolutional layer include the improvement of convolution kernel, activation functions, pooling functions, network structures, etc. However, due to the complexity and unexplainability of convolutional layers, it is difficult to quantitatively analyze and compare the effectiveness of feature extraction. Therefore, this paper proposes the variation coefficient to evaluate the feature learning ability of convolutional layers. We select four typical image steganalysis models based CNN in spatial domain, such as Ye-Net, Yedroudj-Net, Zhu-Net, and SR-Net as use cases, and verify the validity of the variation coefficient through experiments. Moreover, according to the variation coefficient , a features modification layer is used to optimize the features before the fully connected layer of the CNN model , and the experimental results show that the detection accuracy of the four algorithms were improved differently.

Style transfer is a useful image synthesis technique that can re-render given image into another artistic style while preserving its content information. Generative Adversarial Network (GAN) is a widely adopted framework toward this task for its better representation ability on local style patterns than the traditional Gram-matrix based methods. However, most previous methods rely on sufficient amount of pre-collected style images to train the model. In this paper, a novel Patch Permutation GAN (P$^2$-GAN) network that can efficiently learn the stroke style from a single style image is proposed. We use patch permutation to generate multiple training samples from the given style image. A patch discriminator that can simultaneously process patch-wise images and natural images seamlessly is designed. We also propose a local texture descriptor based criterion to quantitatively evaluate the style transfer quality. Experimental results showed that our method can produce finer quality re-renderings from single style image with improved computational efficiency compared with many state-of-the-arts methods., Comment: 5 pages, 5 figures

Manipulating and reserving the valley pseudospin of excitons is one core aim in the two-dimensional transition metal dichalcogenides (TMDs). However, due to the strong electron-hole exchange and spin-orbit coupling interactions, the exciton recombination lifetime is subject to picosecond timescale intrinsically, and the valley polarization is hardly modulated by a moderate magnetic field. It is fortunate that interlayer and defect-localized excitons promise to overcome these difficulties by suppressing the above interactions. Here we clearly reveal that the valley polarization can be reversed and revived in the defect-localized excitons with a microsecond lifetime in AB-stacked WSe2-WS2 heterobilayer. Specifically, for the interlayer defect-localized exciton, the valley polarization is reversed and can be efficiently enhanced by a weak out-of-plane magnetic field (<0.4 T). In sharp contrast, the valley polarization of the intralayer defect-localized exciton can revive after a fast decay process and follows the direction of the moderate out-of-plane magnetic field (<3 T). We explain the reversed valley polarization with highly magnetic sensitivity by the delocalization of defect-localized holes under a weak magnetic field and the revival of valley polarization by the valley Zeeman effect under a moderate magnetic field. Our results demonstrate that the valley pseudospin of defect-localized excitons can be efficiently modulated by the external magnetic field and enrich both the understanding and the technical approaches on manipulating the valley dynamics in TMDs and their heterostructure., Comment: 17 pages, 5 figures

Helium diffusion in metals is the basic requirement of nucleation and growth of bubble, which gives rise to adverse degradation effects on mechanical properties of structural materials in reactors under irradiation. Lattice based Kinetic Monte Carlo approach is widely adopted to study the evolution of helium-vacancy clustering. However, the imple-mentation of Arrhenius law to prediction the event rate of single interstitial helium solute diffusion in metal is not always appropriate due to low-energy barrier. Based on a stochastic model, a modified formula is derived from the Brownian motion upon a cosine-type potential. Using the parameters obtained from molecular dynamics simulation for the diffusivity of single helium solute in BCC W, the prediction of our model is consistent with the results from dynamical simulation and previous model. This work would help to develop a more accurate KMC scheme for the growth of helium-vacancy clusters, as well as other low-energy reactions in materials science.

Helium diffusion in metals is the basic requirement of nucleation and growth of bubble, which gives rise to adverse degradation effects on mechanical properties of structural materials in reactors under irradiation. Multi-scale modeling scheme has been developed to study effects of helium on the long-term microstructural evolution. However, the implementation of Arrhenius law based on the quasi-equilibrium reaction process is not appropriate to predict the migration behavior of helium in metals due to low-energy barrier. A coarse-grained formula is required to incorporate the non-equilibrium nature, e.g., the dissipative friction coefficient $\gamma$. In this paper, we derive an analytical expression for $\gamma$ based on a coarse-grained model of Brownian motion upon a periodic potential, in terms of dissipative feature of the thermal excitations in the many-body dynamical system by constructing an adiabatic relaxation process, which is then confirmed by a numerical example of vacancy migration in BCC W. Then the many-body dynamics simulations are performed for helium migration in BCC W and Fe, where the classical mobility are obtained and in good agreement with the data from experiments and other calculations. Finally, we propose a coarse-grained formula for the helium migration in BCC W and Fe, i.e., Eq.(47) in the context, using the calculated parameters from the adiabatic relaxation simulations. This work would help to develop a new multi-scale modeling scheme for effects of helium in metals, as well as the atomistic reactions with low-energy pathways in materials science.